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Learning R for Geospatial Analysis

You're reading from   Learning R for Geospatial Analysis Leverage the power of R to elegantly manage crucial geospatial analysis tasks

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Product type Paperback
Published in Dec 2014
Publisher Packt
ISBN-13 9781783984367
Length 364 pages
Edition 1st Edition
Languages
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Author (1):
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Michael Dorman Michael Dorman
Author Profile Icon Michael Dorman
Michael Dorman
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Table of Contents (13) Chapters Close

Preface 1. The R Environment FREE CHAPTER 2. Working with Vectors and Time Series 3. Working with Tables 4. Working with Rasters 5. Working with Points, Lines, and Polygons 6. Modifying Rasters and Analyzing Raster Time Series 7. Combining Vector and Raster Datasets 8. Spatial Interpolation of Point Data 9. Advanced Visualization of Spatial Data A. External Datasets Used in Examples
B. Cited References
Index

Using the matrix and array classes


A raster is essentially a matrix with spatial reference information. Similarly, a multiband raster is essentially a three-dimensional array with spatial reference information. Therefore, before proceeding with spatial rasters, we will cover some prerequisite material on working with these (simpler) objects in this section—matrices and arrays. Moreover, as we shall see later, matrices and arrays are common data structures with many uses in R.

Representing two-dimensional data with a matrix

A matrix object is a two-dimensional collection of elements, all of the same type (as opposed to a data.frame object; see the previous chapter), where the number of elements in all rows (and, naturally, all columns) is identical. Matrix objects have many uses in R. For example, certain functions take matrices as their arguments (such as the focal function to filter rasters) or return matrices (such as the extract function to extract raster values; we will meet both these...

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